Compositional (version 5.5)

Transformation-free linear regression for compositional responses and predictors: Transformation-free linear regression for compositional responses and predictors

Description

Transformation-free linear regression for compositional responses and predictors.

Usage

tflr(y, x, xnew = NULL)

Arguments

y

A matrix with the compositional response. Zero values are allowed.

x

A matrix with the compositional predictors. Zero values are allowed.

xnew

If you have new data use it, otherwise leave it NULL.

Value

A list including:

runtime

The time required by the regression.

loglik

The log-likelihood.

be

The beta coefficients.

est

The fitted values of xnew if xnew is not NULL.

Details

The transformation-free linear regression for compositional responses and predictors is implemented. The function to be minized is \(-\sum_{i=1}^ny_i\log{y_i/(X_iB)}\).

References

Jacob Fiksel, Scott Zeger and Abhirup Datta (2020). A transformation-free linear regression for compositional outcomes and predictors. https://arxiv.org/pdf/2004.07881.pdf

See Also

cv.tflr, ols.compcomp kl.alfapcr

Examples

# NOT RUN {
library(MASS)
y <- rdiri(214, runif(3, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
mod <- tflr(y, x, x)
mod
# }